# Regression Models for Time Trends: A Second Example. INSR 260, Spring 2009 Bob Stine

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1 Regression Models for Time Trends: A Second Example INSR 260, Spring 2009 Bob Stine 1

2 Overview Resembles prior textbook occupancy example Time series of revenue, costs and sales at Best Buy, in millions of dollars Quarterly from Similar features Log transformation Seasonal patterns via dummy variables Testing for autocorrelation: Durbin-Watson, lag residuals Prediction with autocorrelation adjustments Novel features Use of segmented model to capture change of regime Decision to set aside some data to get consistent form 2

3 Forecasting Problem Predict revenue at Best Buy for next year Q1, 1995 through Q1, quarters Forecast revenue for the rest of 2008 Estimate forecast accuracy Evident patterns Growth Seasonal Variation Overlay Plot Revenue \$15, \$12, \$10, \$7, \$5, \$2, \$ Time Forecast of profit needs an estimate of cost of goods sold and amount of sales: then difference. 3

4 Initial Modeling Quadratic trend + quarterly seasonal pattern Overall fit is highly statistically significant Summary of Fit RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) Nonetheless model shows problems in residuals Residual by Predicted Plot Revenue Residual Revenue Predicted Residual by Row Plot Residual Row Number Trend in the first quarter of each year (red) appears different from those in other quarters interaction. 4

5 Two Ways to Fix Two approaches Add interactions that allow slopes to differ by quarter Do you want to predict quadratic growth? Log transformation Use log Curvature remains, but variance seems stable with consistent patterns in the quarters Overlay Plot Revenue Time 5

6 Model on Log Scale Model of logs on time and quarter is highly statistically significant, Summary of Fit RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) Indicator Function Parameterization Term Intercept Time Quarter[1] Quarter[2] Quarter[3] Estimate Std Error DFDen t Ratio But residuals show lack of fit and dependence Prob> t * Residual by Predicted Plot Log Revenue Residual Log Revenue Predicted Residual by Row Plot Residual Row Number Why does slope (% growth rate) seem to change? 6

7 Modified Trend Introduce period dummy variable Exclude first two years of data (8 quarters) Add Pre-Post Dot Com indicator Allows slope to shift at start of 2002 Another shift is possible! 2002 Better model? Summary statistics Indicator Function Parameterization Summary of Fit RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) Residual plots Term Intercept Time Quarter[1] Quarter[2] Quarter[3] Pre/Post Dot Com[post] Time*Pre/Post Dot Com[post] Estimate Std Error DFDen t Ratio Prob> t Residual by Predicted Plot Log Revenue Residual Residual by Row Plot Residual Huge shift in rate of growth Log Revenue Predicted Row Number 7

8 Autocorrelation? Dependence absent from sequence plot Confirmed by Durbin-Watson, residual scatterplot Durbin-Watson Durbin- Number Watson of Obs AutoCorrelation Prob<DW Residual Log Revenue Lag Residuals No need to add lagged residual as explanatory variable; all captured by trend + seasonal Indicator Function Parameterization Term Intercept Time Quarter[1] Quarter[2] Quarter[3] Pre/Post Dot Com[post] Time*Pre/Post Dot Com[post] Lag Residuals Estimate Std Error DFDen t Ratio Prob> t

9 More Diagnostics Residual plots show little remaining structure Similar variances in quarters? Residual Log Revenue Quarter Normality seems reasonable (albeit outliers in Q1) Count Normal Quantile Plot 9

10 Forecasting Forecast log revenue for rest of 2008 ŷ n+j = ( Q j ) + " " " " " seasonal " " ( ) time" " time trend Overall intercept plus adjustment for pre/post Examples for Q2, Q3, Q4 of 2008 ŷ 53+1 = ( )" " " " Q 2 = " " ( ) " " ŷ 53+2 = ( )" " " Q 3 = " " ( ) " " ŷ 53+3 = ( )"" " " " " " Q 4 = 0 " " ( ) " "

11 Forecast Accuracy Since model does not have autocorrelation and data meet assumptions of MRM, we can use the JMP prediction intervals One period out ŷ 53+1 ± t.025 SE(indiv pred) = to Two periods out ŷ 53+2 ±t.025 SE(indiv pred) = "9.1363" Three periods out ŷ 53+3 ±t.025 SE(indiv pred) = "9.2510"

12 Prediction Intervals Obtain predictions of revenue, not the log of revenue Conversion Form interval as we have done on transformed scale Exponentiate " " to " " "" e to e " " " " " " " " " " " " \$8446 to \$9497 (million) As in prior example, the prediction interval is much wider than you may have expected from the R 2 and RMSE of the model on the log scale. Small differences on log scale are magnified on \$ scale 12

13 Alternative Segments Prior approach adds two variables to segment Dummy variable for period allows new intercept Interaction allows slope to change Models fit in the two periods are disconnected Not constrained to be continuous or intersect where the second period begins Alternative approach forces continuity Add one parameter for change in the slope No dummy variable needed. Intercept defined by the location of the prior fit. Pre Post 13

14 Building the Variables Model comparison Break in structure (kink) at time T Before (t T) : Y t = β 0 + β 1 X t + ε t After (t > T) : Y t = α 0 + (β 1 + δ)x t + ε t Choose α 0 so that means match at time T " β 0 + β 1 X T "= α 0 + (β 1 + δ)x T α 0 = β 0 - δx T Hence, only need to estimate one parameter, δ To fit with regression, add the variable Z t Z t = 0 for t T, Z t = X t - X T for t > T Before T: no effect on the fit since 0 After T: β 0 + β 1 X t + δ Z t = β 0 + β 1 X t + δ (X t - X T ) " " " " " " " " " = (β 0 - δx T ) + (β 1 +δ) X t 14

15 Changing the Slope Added variable is very simple Prior to the change point, it s 0 After the change point, its (x - time of change) Picture shows dog-leg shape of new variable with kink at the change point New Variable 15

16 Example Fit with distinct segments Indicator Function Parameterization Summary of Fit RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) Term Intercept Time Quarter[1] Quarter[2] Quarter[3] Pre/Post Dot Com[post] Time*Pre/Post Dot Com[post] Estimate Std Error DFDen t Ratio Prob> t Fit with continuous joint Almost as large R 2, with one less estimated parameter Similar shift in slope in two models. Indicator Function Parameterization Summary of Fit RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) Term Intercept Time Time Post Quarter[1] Quarter[2] Quarter[3] Estimate Std Error DFDen t Ratio Prob> t 16

17 Summary A basic trend (linear, perhaps quadratic) plus dummy variables is a good starting model for many time series that show increasing levels. Log transformations stabilize the variation, are easily interpreted, and avoid more complicated trends and interactions. Dummy variables can model a trend break. Models do not anticipate the time of another trend break in the future. Special broken line variable models shift in slope with one parameter, forcing continuity. R 2 is misleading when you see the prediction intervals when fitting on a log scale. 17

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